为了解决毫米波无线通信中模数转换器(analog to digital converter,ADC)无法在接收信号上实现高倍的过采样以及多径影响所导致的定时跟踪问题,该文基于2倍过采样数据,根据相关波形,利用Farrow插值给出2种适用于多径信道的定时跟踪方案...为了解决毫米波无线通信中模数转换器(analog to digital converter,ADC)无法在接收信号上实现高倍的过采样以及多径影响所导致的定时跟踪问题,该文基于2倍过采样数据,根据相关波形,利用Farrow插值给出2种适用于多径信道的定时跟踪方案,分别在频域均衡之前和之后进行误差精补偿。仿真结果表明:在Rummler信道下,定时频偏为时钟频率的20×10^(-6)、误比特率为10^(-5)时,这2种方案的信噪比(signal-to-noise ratio,SNR)与无定时频偏时的只相差2.5dB左右,说明这2种方案在多径信道下具有良好的定时跟踪性能。展开更多
Since a lot of engineering problems are along with uncertain parameters, stochastic methods are of great importance for incorporating random nature of a system property or random nature of a system input. In this stud...Since a lot of engineering problems are along with uncertain parameters, stochastic methods are of great importance for incorporating random nature of a system property or random nature of a system input. In this study, the stochastic dynamic analysis of soil mass is performed by finite element method in the frequency domain. Two methods are used for stochastic analysis of soil media which are spectral decomposition and Monte Carlo methods. Shear modulus of soil is considered as a random field and the seismic excitation is also imposed as a random process. In this research, artificial neural network is proposed and added to Monte Carlo method for sake of reducing computational effort of the random analysis. Then, the effects of the proposed artificial neural network are illustrated on decreasing computational time of Monte Carlo simulations in comparison with standard Monte Carlo and spectral decomposition methods. Numerical verifications are provided to indicate capabilities, accuracy and efficiency of the proposed strategy compared to the other techniques.展开更多
文摘为了解决毫米波无线通信中模数转换器(analog to digital converter,ADC)无法在接收信号上实现高倍的过采样以及多径影响所导致的定时跟踪问题,该文基于2倍过采样数据,根据相关波形,利用Farrow插值给出2种适用于多径信道的定时跟踪方案,分别在频域均衡之前和之后进行误差精补偿。仿真结果表明:在Rummler信道下,定时频偏为时钟频率的20×10^(-6)、误比特率为10^(-5)时,这2种方案的信噪比(signal-to-noise ratio,SNR)与无定时频偏时的只相差2.5dB左右,说明这2种方案在多径信道下具有良好的定时跟踪性能。
文摘Since a lot of engineering problems are along with uncertain parameters, stochastic methods are of great importance for incorporating random nature of a system property or random nature of a system input. In this study, the stochastic dynamic analysis of soil mass is performed by finite element method in the frequency domain. Two methods are used for stochastic analysis of soil media which are spectral decomposition and Monte Carlo methods. Shear modulus of soil is considered as a random field and the seismic excitation is also imposed as a random process. In this research, artificial neural network is proposed and added to Monte Carlo method for sake of reducing computational effort of the random analysis. Then, the effects of the proposed artificial neural network are illustrated on decreasing computational time of Monte Carlo simulations in comparison with standard Monte Carlo and spectral decomposition methods. Numerical verifications are provided to indicate capabilities, accuracy and efficiency of the proposed strategy compared to the other techniques.
